The significant portion of diabetic patients was affected due to major blindness caused by Diabetic retinopathy (DR). For diabetic retinopathy, lesion segmentation, and detection the comprehensive examination is delved into the deep learning techniques application. The study conducted a systematic literature review using the PRISMA analysis and 62 articles has been investigated in the research. By including CNN-based models for DR grading, and feature fusion several deep-learning methodologies are explored during the study. For enhancing effectiveness in classification accuracy and robustness the data augmentation and ensemble learning strategies are scrutinized. By demonstrating the superior performance compared to individual models the efficacy of ensemble learning methods is investigated. The potential ensemble approaches in DR diagnosis are shown by the integration of multiple pre-trained networks with custom classifiers that yield high specificity. The diverse deep-learning techniques that are employed for detecting DR lesions are discussed within the diabetic retinopathy lesions segmentation and detection section. By emphasizing the requirement for continued research and integration into clinical practice deep learning shows promise for personalized healthcare and early detection of diabetics.

翻译：糖尿病视网膜病变（DR）是导致糖尿病患者失明的主要原因，影响大量患者群体。本研究针对糖尿病视网膜病变的分级、病灶分割与检测，深入探讨了深度学习技术的应用。研究采用PRISMA分析方法进行系统性文献综述，共纳入62篇相关论文进行分析。研究过程中探索了多种深度学习方法，包括基于CNN的DR分级模型和特征融合技术。为提高分类准确率与鲁棒性，研究详细审视了数据增强和集成学习策略。通过展示其相较于单一模型的优越性能，本研究验证了集成学习方法的有效性。将多个预训练网络与定制分类器相结合，实现了高特异性诊断，展现了集成方法在DR诊断中的潜力。在糖尿病视网膜病变病灶分割与检测部分，讨论了用于DR病灶检测的多种深度学习技术。深度学习技术展现出在个性化医疗和糖尿病早期检测中的应用前景，同时强调了持续研究和临床实践融合的必要性。